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Record W2317411584 · doi:10.1109/tpel.2014.2310955

Investigation and Suppression of Harmonics Interaction in High-Power PWM Current-Source Motor Drives

2014· article· en· W2317411584 on OpenAlexaff
Ye Zhang, Yunwei Li

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2014
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHarmonicsChokePWM rectifierPulse-width modulationControl theory (sociology)Current sourceRectifier (neural networks)Power (physics)DC motorElectrical impedanceEngineeringComputer scienceCurrent (fluid)PhysicsElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

For high-power PWM current-source drive systems, the distortion of dc-link current cannot be ignored due to the low converter switching frequency and the relative small dc choke for reduced cost/weight. The distorted dc-link current may introduce interharmonics in the grid side and the motor side through the rectifier and the inverter, and may give rise to the system resonance at certain motor speeds. When resonance occurs, significant interharmonics are introduced in the drive system, which will degrade the grid-side power quality and produce the excessive torque ripples on the motor side. In this paper, the harmonics interaction in a high-power PWM current-source drive system is investigated, and a method for the system resonance estimation is proposed. Based on the estimation of resonance conditions, a dc-link virtual impedance-based control method is developed to mitigate the resonance resulted from the harmonics interaction. The investigation of harmonics interaction, the proposed resonance estimation method, and the dc-link virtual impedance-based suppression method are verified through both simulations and experiments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.802

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.213
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations53
Published2014
Admission routes1
Has abstractyes

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